#other#agent#ai#artificial_intelligence#autogpt#autonomous_agents#awesome#babyagi#copilot#gpt#gpt_4#gpt_engineer#openai#python
Codeium is a free AI-powered coding assistant that helps you write code faster and better by providing real-time autocomplete suggestions, generating code from natural language, explaining code, and assisting with refactoring. It supports over 70 programming languages and integrates with many popular IDEs like Visual Studio Code. Codeium learns from your coding style and project context to offer relevant suggestions, saving you time and reducing errors. It also includes a chat feature to answer coding questions instantly, so you don’t need to switch to a browser for help. This boosts your productivity and code quality efficiently.
https://github.com/e2b-dev/awesome-ai-agents
#DL
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
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A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
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There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
#dl
This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
#dl
Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948